Measures of heart rate variability (HRV) have been shown to be a powerful means of assessing the influences of autonomic tone on cardiac function (8). However, because some methodological problems exist in obtaining reliable estimates of this measure, the practical application of HRV is limited, and the interpretation of such results is confounded. For example, in time domain analysis, measures of dispersion, such as the standard deviation, increase with increasing data length, making cross-study comparisons difficult. Further, dispersion measures also do not take into account the degree of temporal autocorrelation.
Conventionally, problems due to autocorrelations can be avoided by conducting the analysis in the frequency domain using a fast Fourier transform. However, this method assumes that for the epoch investigated, the time series remains stationary. This assumption is less likely to hold true as longer time intervals are sampled. On the other hand, short data lengths are also problematic because the contributions of low frequencies to the overall power spectrum cannot properly be estimated. Consequently, the outcomes of applying time domain analysis or frequency domain analysis are not easily interpreted when the recording time of the HRV is relatively short, such as less than five minutes, or when the length of the recorded time series varies significantly between individuals. Previous work suggests a strong correlation between alcohol dependence and altered heart rate dynamics. Heart rate dynamics are usually estimated using parameters obtained from time-domain or frequency-domain analyses (11, 12). Results obtained with these methods are confounded by the changing statistical properties of heartbeat interval time series over time, called non-stationary signals. These signals are difficult to interpret with dispersional measures in the time-domain, such as the standard deviation, because the results are not stable with increasing data length. These measures also provide no information regarding the internal dynamics of the time series. Frequency-domain measures rely on assumptions of stationarity that are not met with interbeat interval (IBI) time series data, especially with longer recording times.